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Record W1990369218 · doi:10.1139/i07-012

Laser peripheral iridotomy across the spectrum of primary angle closure

2007· article· en· W1990369218 on OpenAlexvenueno aff
Surinder Singh Pandav, Sushmita Kaushik, Rajeev Jain, Reema Bansal, Amod Gupta

Bibliographic record

VenueCanadian Journal of Ophthalmology · 2007
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOphthalmologyOdds ratioGlaucomaRetrospective cohort studyClosure (psychology)SurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: To study the effectiveness of Nd:YAG laser peripheral iridotomy (LPI) for primary angle closure in Asian Indian patients. METHODS: Retrospective analyses of patients who underwent LPI and completed a minimum follow-up of 2 years. Eyes were classified as primary angle-closure suspects (PACS), primary angle closure (PAC), and primary angle-closure glaucoma (PACG). The indications for LPI, requirement of medication, and subsequent clinical course were studied in each group. RESULTS: 103 eyes of 55 patients were analyzed. The mean (SD) follow-up was 45.6 (2) months. The mean age in women was less than in men (55.7 [8.3] vs. 62.1 [7.8] years).Twenty-seven eyes were classified as PACS, 43 eyes as PAC, and 33 eyes as PACG. After LPI, no eye with PACS progressed to PAC or PACG. Four of 43 eyes (9.3%) with PAC progressed to PACG.Twenty-five of the 33 eyes (75.8%) with PACG did not progress after LPI during the study period. Patients with <or=2 quadrants of angle closure at baseline had 7.7% odds of progression compared with 100% odds in patients with >2 quadrants of angle closure (risk ratio 12.9). INTERPRETATION: After LPI, the rate of progression from PAC to PACG was less than expected from the reported natural course of the disease, and the majority of eyes with PACG remained stable. LPI appears to alter the natural course of PACS, PAC, and PACG favourably.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2007
Admission routes1
Has abstractyes

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